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Tools for interpreting and generating new climate data

Project description

# OCR Tools Open Climate Research is an ongoing project that aims to facilitate creative experimentation with modeled climate data. OCR Tools aims to be much more than a climate data viewer by enabling non-scientists to utilize a wide range of datasets and providing users with simple feedback conducive to learning. In addition to providing basic analysis functions, OCR Tools includes organizational and creative tools.

## Installing / Getting started

Run the following to install: `python pip install ocrtools `

## Examples

  • Open a NetCDF dataset with

  • ` python import ocrtools as ocr cesm_TS = ocr.load('path/to/cesm_TS_data.nc', var='TS') `

    If var is omitted, ocrtools will print out all variables in the dataset and ask you to specify a variable(s) of interest via command line. The dataset is then opened as an Xarray Dataset

  • Create a scope object

    ` python lima_peru = ocr.scope(location='Lima, Peru', yr0=1950, yrf=2000) `

    • Location can also be specified by keyword arguments lat_min, lat_max, lon_min, and lon_max; or if none of these are given, location can be specified interactively by selecting areas on a map
  • Subset your data

    `python lima_TS = ocr.subset(cesm_TS, lima_peru) `

  • Select an area on a map and take the spatial average

    `python from ocrtools import plt map_selection = ocr.scope() `

    `shell [OCR] Creating new scope object Enter yr0: Enter yrf: Select area(s) on map and close the pop-up window `

    <img src=”http://andreschang.com/unlinked/tk_selector_screenshot.png” width=70%/>

`shell [OCR] Finished writing new scope object `

`python peru_TS = ocr.subset(cesm_TS, map_selection) peru_avg_TS = ocr.spatial_average(peru_TS) peru_avg_TS['TS'].plot() plt.show() `

Project details


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